Facial image generating method, facial image generating apparatus, and facial image generating device

ABSTRACT

A facial image generating method, a facial image generating apparatus, and a facial image generating device are proposed. The method comprises: linking an M-dimensional facial feature vector with an N-dimensional demanded feature vector to generate a synthesized feature vector; and generating a synthesized facial image by use of a deep convolutional network for facial image generation and based on the synthesized feature vector. The method further comprises: generating a demand satisfaction score based on the synthesized facial image and the demanded feature vector by use of a deep convolutional network for demand determination; and updating parameters of the deep convolutional network for facial image generation and the deep convolutional network for demand determination based on the demand satisfaction score. A facial image is generated based on a facial feature vector and a demanded feature vector, a facial image with a specific feature can be generated without using the three-dimensional model.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to Chinese patent application No.201611008893.X filed on Nov. 16, 2016, the entire contents of which areincorporated herein by reference.

TECHNICAL FIELD

The present disclosure relates to the technical field of facial imagegeneration, and more particularly to a facial image generating method, afacial image generating apparatus, and a facial image generating device.

BACKGROUND

In the current facial image generation and facial reconstruction, athree-dimensional model is generated from a plurality of two-dimensionalpictures of different view angles, and then rendering is performed toobtain a new two-dimensional image.

However, this method of obtaining a new two-dimensional image based on athree-dimensional model has large time-out and low efficiency, and itneeds to use pictures of different angles of the same individual toperform three-dimensional modeling, which usually cannot be satisfied inpractice. Moreover, this method can only be applied to generation of animage of an already-existing individual at a different angle and withdifferent lighting condition, and this method cannot generate a newfacial image.

Therefore, there is a need for a method and an apparatus capable of notonly reconstructing, based on a demanded feature, a facial image withthe demanded feature from an existing facial image, but also generatinga new facial image based on only the demanded feature without basing onthe existing facial image.

SUMMARY

In view of the above problem, the present disclosure is proposed toprovide a facial image generating method and a facial image generatingapparatus, a facial image is generated by use of a deep convolutionalnetwork for facial image generation based on a facial feature vector anda demanded feature vector, a facial image with a specific feature can begenerated without using the three-dimensional model.

According to an aspect of the present disclosure, there is provided afacial image generating method, comprising: generating an M-dimensionalfacial feature vector, M being an integer larger than one; linking theM-dimensional facial feature vector with an N-dimensional demandedfeature vector to generate a synthesized feature vector, N being aninteger larger than or equal to one; and generating a synthesized facialimage by use of a deep convolutional network for facial image generationand based on the synthesized feature vector.

In addition, according to an embodiment of the present disclosure, thefacial image generating method further comprises: generating a demandsatisfaction score based on the synthesized facial image and thedemanded feature vector and by use of a deep convolutional network fordemand determination; and updating parameters of the deep convolutionalnetwork for facial image generation and the deep convolutional networkfor demand determination based on the demand satisfaction score.

In addition, according to an embodiment of the present disclosure, inthe case of extracting the facial feature vector from the given facialimage, the facial image generating method further comprises: generatinga face matching score based on the synthesized facial image and thegiven facial image and by use of a first deep convolutional network forface determination; and updating parameters of the deep convolutionalnetwork for facial feature extraction, the deep convolutional networkfor facial image generation, and the first deep convolutional networkfor face determination based on the face matching score.

In addition, according to an embodiment of the present disclosure, inthe case of randomly generating the facial feature vector, the facialimage generating method further comprises: generating a facesatisfaction score based on the synthesized facial image and by use of asecond deep convolutional network for face determination; and updatingparameters of the deep convolutional network for facial image generationand the second deep convolutional network for face determination basedon the face satisfaction score.

According to another aspect of the present disclosure, there is provideda facial image generating apparatus, comprising: a facial featuregenerating module configured to generate an M-dimensional facial featurevector, M being an integer larger than one; a vector synthesizing moduleconfigured to link the M-dimensional facial feature vector with anN-dimensional demanded feature vector to generate a synthesized featurevector, N being an integer larger than or equal to one; and asynthesized facial image generating module configured to generate asynthesized facial image by use of a deep convolutional network forfacial image generation and based on the synthesized feature vector.

In addition, according to an embodiment of the present disclosure, thefacial image generating apparatus further comprises: a demanddetermining module configured to generate a demand satisfaction scorebased on the synthesized facial image and the demanded feature vectorand by use of a deep convolutional network for demand determination; anda first parameter adjusting module configured to update parameters ofthe deep convolutional network for facial image generation and the deepconvolutional network for demand determination based on the demandsatisfaction score.

In addition, according to an embodiment of the present disclosure, inthe case of extracting the facial feature vector from the given facialimage, the facial image generating apparatus further comprises: a firstface determining module configured to generate a face matching scorebased on the synthesized facial image and the given facial image and byuse of a first deep convolutional network for face determination; and asecond parameter adjusting module configured to update parameters of thedeep convolutional network for facial feature extraction, the deepconvolutional network for facial image generation, and the first deepconvolutional network for face determination based on the face matchingscore.

In addition, according to an embodiment of the present disclosure, inthe case of randomly generating the facial feature vector, the facialimage generating apparatus further comprises: a second face determiningmodule configured to generate a face satisfaction score based on thesynthesized facial image and by use of a second deep convolutionalnetwork for face determination; and a third parameter adjusting moduleconfigured to update parameters of the deep convolutional network forfacial image generation and the second deep convolutional network forface determination based on the face satisfaction score.

According to an embodiment of the present disclosure, there is provideda facial image generating device, comprising: one or more processors;one or more memories for storing program instructions, the programinstructions being executed by the processors to perform the followingsteps: generating an M-dimensional facial feature vector, M being aninteger larger than one; linking the M-dimensional facial feature vectorwith an N-dimensional demanded feature vector to generate a synthesizedfeature vector, N being an integer larger than or equal to one; andgenerating a synthesized facial image by use of a deep convolutionalnetwork for facial image generation and based on the synthesized featurevector.

According to an embodiment of the present disclosure, a facial image isgenerated by use of a deep convolutional network for facial imagegeneration and based on a facial feature vector and a demanded featurevector, a facial image with a specific feature prescribed by thedemanded feature vector can be generated fast without using thethree-dimensional model.

In addition, according to an embodiment of the present disclosure, aftera synthesized facial image is generated, whether the generatedsynthesized facial image satisfies requirements of the demanded featurevector is determined and the corresponding demand satisfaction score isgenerated by use of the deep convolutional network for demanddetermination, and further, parameters of the deep convolutional networkfor facial image generation and the deep convolutional network fordemand determination are updated based on the demand satisfaction score,thereby the deep convolutional network for facial image generation andthe deep convolutional network for demand determination can be trainedin synchronization.

In addition, according to an embodiment of the present disclosure, aftera synthesized facial image is generated, whether the generatedsynthesized facial image is a facial image or whether the generatedsynthesized facial image and the given facial image belong to the sameface is determined and the corresponding face satisfaction score or facematching score is generated by use of the deep convolutional network forface determination, and further, parameters of the deep convolutionalnetwork for facial image generation and the deep convolutional networkfor face determination are updated based on the generated facesatisfaction score or the generated face matching score, thereby thedeep convolutional network for facial image generation and the deepconvolutional network for face determination can be trained insynchronization.

BRIEF DESCRIPTION OF THE DRAWINGS

Through the more detailed description of the embodiments of the presentdisclosure in combination with the accompanying drawings, the above andother objects, features, and advantages of the present disclosure willbecome more apparent. The drawings are shown to provide furtherunderstanding for the embodiments of the present disclosure andconstitute a portion of the specification, and are intended toillustrate the present disclosure together with the embodiments ratherthan to limit the present disclosure. In the drawings, the samereference sign generally refers to the same component or step.

FIG. 1 is a schematic flowchart of a facial image generating methodaccording to an embodiment of the present disclosure;

FIG. 2 is a schematic diagram of the principle of generating asynthesized facial image based on a facial feature vector and a demandedfeature vector according to an embodiment of the present disclosure;

FIG. 3A is a schematic diagram of structure of a deep convolutionalnetwork for facial image generation according to an embodiment of thepresent disclosure;

FIG. 3B is another schematic diagram of structure of a deepconvolutional network for facial image generation according to anembodiment of the present disclosure;

FIG. 4A is a schematic diagram of structure of an integratedconvolutional neural network corresponding to FIG. 3A according to anembodiment of the present disclosure;

FIG. 4B is a schematic diagram of structure of an integratedconvolutional neural network corresponding to FIG. 3B according to anembodiment of the present disclosure;

FIG. 4C is another schematic diagram of structure of an integratedconvolutional neural network corresponding to FIG. 3B according to anembodiment of the present disclosure;

FIG. 5A is a schematic diagram of the principle of facial imagegeneration and determination process according to a first embodiment ofthe present disclosure;

FIG. 5B is a schematic diagram of the principle of facial imagegeneration and determination process according to a second embodiment ofthe present disclosure;

FIG. 6 is a schematic diagram of structure of a deep convolutionalnetwork for facial feature extraction according to the first embodimentof the present disclosure;

FIG. 7A is a schematic block diagram of structure of a first deepconvolutional network for face determination according to the firstembodiment of the present disclosure;

FIG. 7B is a schematic block diagram of structure of a second deepconvolutional network for face determination according to the secondembodiment of the present disclosure;

FIG. 8 is a schematic diagram of structure of a to-be-determined featurevector extraction network according to an embodiment of the presentdisclosure;

FIG. 9 is a schematic diagram of structure of a deep convolutionalnetwork for demand determination according to an embodiment of thepresent disclosure;

FIG. 10 is a schematic block diagram of a facial image generatingapparatus according to an embodiment of the present disclosure;

FIG. 11A is a schematic block diagram of a facial image generating anddetermining apparatus according to the first embodiment of the presentdisclosure;

FIG. 11B is a schematic block diagram of a facial image generating anddetermining apparatus according to the second embodiment of the presentdisclosure; and

FIG. 12 is a schematic block diagram of an electronic device in which afacial image generating and determining apparatus according to anembodiment of the present disclosure is implemented.

DETAILED DESCRIPTION OF THE EMBODIMENTS

To make the objectives, technical solutions, and advantages of thepresent disclosure more clear, exemplary embodiments of the presentdisclosure will be described in detail with reference to theaccompanying drawings. Obviously, the described embodiments merely arepart of the embodiments of the present disclosure, rather than all ofthe embodiments of the present disclosure, it should be understood thatthe present disclosure is not limited to the exemplary embodimentsdescribed herein. All other embodiments obtained by those skilled in theart without paying inventive efforts should all fall into the protectionscope of the present disclosure.

FIG. 1 is a schematic flowchart of a facial image generating method 100according to an embodiment of the present disclosure.

In step S110, an M-dimensional facial feature vector is generated, Mbeing an integer larger than one. Preferably, the facial feature vectoris a high dimensional feature vector.

According to an embodiment of the present disclosure, the facial featurevector may be extracted from a given facial image. For example, thefacial feature vector may be extracted from a given facial image by useof a deep convolutional network for facial feature extraction accordingto an embodiment of the present disclosure. Alternatively, according toan embodiment of the present invention, the facial feature vector may berandomly generated, and in this case, the facial feature vectorrepresents a randomly generated face.

For example, a value of each dimension of the M-dimensional facialfeature vector is a real number in a range of (−1, 1) or a real numberin a range of (0, 1).

In step S120, the M-dimensional facial feature vector is linked with anN-dimensional demanded feature vector to generate a synthesized featurevector, N being an integer larger than or equal to one.

For example, a value of each dimension of the N-dimensional demandedfeature vector is a real number in a range of (−1, 1) or a real numberin a range of (0, 1), and represents a different demand, for example,whether glasses were worn, whether there are bangs, light intensity,face rotation angle and so on.

According to an embodiment of the present disclosure, the M-dimensionalfacial feature vector is linked with an N-dimensional demanded featurevector to generate an (M+N)-dimensional synthesized feature vector. Forexample, the N-dimensional demanded feature vector may be placed afterthe M-dimensional facial feature vector or before the M-dimensionalfacial feature vector, or may be inserted into the M-dimensional facialfeature vector.

In step S130, a synthesized facial image is generated by use of a deepconvolutional network for facial image generation and based on thesynthesized feature vector.

FIG. 2 shows a schematic diagram of the principle of generating asynthesized facial image based on a facial feature vector and a demandedfeature vector according to an embodiment of the present disclosure.

First, the facial feature vector and the demanded feature vector arelinked to generate a synthesized feature vector. For example, anM-dimensional facial feature vector and an N-dimensional demanded vectorfeature are linked to generate an (M+N)-dimensional synthesized featurevector. Thereafter, the deep convolutional network for facial imagegeneration generates a synthesized facial image based on the(M+N)-dimensional synthesized feature vector.

FIG. 3A shows a schematic diagram of structure of a deep convolutionalnetwork for facial image generation according to an embodiment of thepresent disclosure, FIG. 3B shows another schematic diagram of structureof a deep convolutional network for facial image generation according toan embodiment of the present disclosure.

As shown in FIGS. 3A and 3B, the deep convolutional network for facialimage generation comprises at least one layer of fully connected neuralnetwork and K layers of integrated convolutional neural network, K beingan integer larger than or equal to two. The number of layers K of theintegrated convolutional neural network is determined by a size of thesynthesized facial image, the bigger the size of the synthesized facialimage is, the higher the number of layers K of the integratedconvolutional neural network is. For example, the size of thesynthesized facial image is 256×256, the required number of layers ofthe integrated convolutional neural network is 3 layers; the size of thesynthesized facial image is 128×128, the required number of layers ofthe integrated convolutional neural network is 2 layers.

As shown in FIG. 3A, the at least one layer of fully connected neuralnetwork is used to receive the synthesized feature vector, and generateinitial synthesized images based on the synthesized feature vector.Thereafter, a first layer of integrated convolutional neural networkreceives the initial synthesized images outputted from the at least onelayer of fully connected neural network and generates synthesized imagesof the first layer; a k-th layer of integrated convolutional neuralnetwork receives synthesized images of a (k−1)-th layer outputted fromthe (k−1)-th layer of integrated convolutional neural network andgenerates synthesized images of the k-th layer, k being an integerlarger than or equal to two and smaller than or equal to K, a size(resolution) of the synthesized images of the k-th layer is larger thana size (resolution) of the synthesized images of the (k−1)-th layer, anda number (channel number) of the synthesized images of the k-th layer issmaller than a number (channel number) of the synthesized images of the(k−1)-th layer; last, a K-th layer of integrated convolutional neuralnetwork receives synthesized images of a (K−1)-th layer outputted from a(K−1)-th layer of integrated convolutional neural network and generatessynthesized images of a K-th layer, and the synthesized images of theK-th layer is taken as the synthesized facial image.

As shown in FIG. 3B, the at least one layer of fully connected neuralnetwork is used to receive the synthesized feature vector, and generateinitial synthesized images based on the synthesized feature vector.Thereafter, a first layer of integrated convolutional neural network notonly can receive the initial synthesized images from the at least onelayer of fully connected neural network but also can receive initialmapped images generated by mapping from the demanded feature vector, andgenerate synthesized images of the first layer based on the initialsynthesized images and the initial mapped images, a size of the initialsynthesized images is the same as a size of the initial mapped images;likewise, a k-th layer of integrated convolutional neural network notonly can receive synthesized images of a (k−1)-th layer outputted fromthe (k−1)-th layer of integrated convolutional neural network, but alsocan receive mapped images of the (k−1)-th layer generated by mappingfrom the demanded feature vector, and generate synthesized images of thek-th layer based on the synthesized images of the (k−1)-th layer and themapped images of the (k−1)-th layer, a size of the synthesized images ofthe (k−1)-th layer is the same as a size of the mapped images of the(k−1)-th layer, a size (resolution) of the synthesized images of thek-th layer is larger than a size (resolution) of the synthesized imagesof the (k−1)-th layer, and a number (channel number) of the synthesizedimages of the k-th layer is smaller than a number (channel number) ofthe synthesized images of the (k−1)-th layer; last, a K-th layer ofintegrated convolutional neural network receives synthesized images of a(K−1)-th layer as outputted from the (K−1)-th layer of integratedconvolutional neural network, and also receives mapped images of the(K−1)-th layer generated by mapping from the demanded feature vector,and generates the synthesized images of the K-th layer based on thesynthesized images of the (K−1)-th layer and the mapped images of the(K−1)-th layer, and the synthesized images of the K-th layer is taken asthe synthesized facial image, a size of the synthesized images of the(K−1)-th layer is the same as a size of the mapped images of the (K−1)thlayer.

As will be appreciated, the initial synthesized images comprise aplurality of initial synthesized images, for example, 128 (channels)initial synthesized images with a size (resolution) of 32×32; theinitial mapped images comprise N initial mapped images, each dimensionalof the N-dimensional demanded feature vector corresponds to one of theinitial mapped images, separately. For example, a value of a certaindimension of the N-dimensional demanded feature vector is a, then avalue of each pixel of the corresponding initial mapped images with asize of 32×32 is a. Hereinafter, for the sake of simplification anduniformity, the initial synthesized images are referred to as thesynthesized images of the zero layer and the initial mapped images isreferred to as the mapped images of the zero layer.

As will be appreciated, any layer (the k-th layer, k being an integerlarger than or equal to one and smaller than or equal to K) ofintegrated convolutional neural network in the K layers of integratedconvolutional neural network can generate the synthesized images of thislayer based on the synthesized images received by it, or generate thesynthesized images of this layer based on the synthesized images of the(k−1)-th layer and the mapped images of the (k−1)-th layer received byit. In addition, it should be understood that, the mapped images of the(k−1)-th layer also comprises N mapped images of the (k−1)-th layer,each dimension of the N-dimensional demanded feature vector correspondsto one of the mapped images of the (k−1)-th layer, separately.

FIG. 4A shows a schematic diagram of structure of an integratedconvolutional neural network corresponding to FIG. 3A according to anembodiment of the present disclosure.

As shown in FIG. 4A, each layer of integrated convolutional neuralnetwork comprises an amplification network and J layers of convolutionalneural networks, J being an integer larger than or equal to two.Hereinafter, for convenience of description, the integratedconvolutional neural network shown in FIG. 4A is referred to as theintegrated convolutional neural network of the k-th layer.

Corresponding to the deep neural network for facial image generationshown in FIG. 3A, the integrated convolutional neural network of thek-th layer as shown in FIG. 4A receives synthesized images of a (k−1)-thlayer, k being an integer larger than or equal to one and smaller thanor equal to K. Specifically, the amplification network receives thesynthesized images of the (k−1)-th layer and amplifies the synthesizedimages of the (k−1)-th layer to generate amplified images; thereafter, afirst layer of convolutional neural network receives the amplifiedimages and generates intermediate images of the first layer; likewise, aj-th layer of convolutional neural network receives intermediate imagesof a (j−1)-th layer from the (j−1)-th layer of convolutional neuralnetwork and generates intermediate images of the j-th layer, a size ofthe intermediate images of the j-th layer is the same as a size of theintermediate images of the (j−1)-th layer, a number (channel number) ofthe intermediate images of the j-th layer may be larger than, equal to,or smaller than a number (channel number) of the intermediate images ofthe (j−1)-th layer, j being an integer larger than or equal to two andsmaller than or equal to J; last, a J-th layer of convolutional neuralnetwork receives intermediate images of a (J−1)-th layer and generatesintermediate images of the J-th layer, which is taken as synthesizedimages of the k-th layer outputted by the k-th layer of integratedconvolutional neural network.

For example, the amplification network amplifies the receivedsynthesized images of the (k−1)-th layer two times, that is, it isassumed that a size of the synthesized images of the (k−1)-th layer is32×32, then a size of the amplified images generated by theamplification network is 64×64. It should be understood that, a number(channel number) of the amplified images generated by the amplificationnetwork is the same as a number (channel number) the synthesized imagesof the (k−1)-th layer, and a number (channel number) of the synthesizedimages of the k-th layer as generated by the k-th layer of integratedconvolutional neural network is smaller than a number (channel number)of the synthesized images of the (k−1)-th layer. For example, the number(channel number) of the synthesized images of the k-th layer asgenerated by the k-th layer of integrated convolutional neural networkis usually ½, ⅓ and so on of the number (channel number) of thesynthesized images of the (k−1)-th layer.

FIG. 4B shows a schematic diagram of structure of an integratedconvolutional neural network corresponding to FIG. 3B according to anembodiment of the present disclosure.

As shown in FIG. 4B, each layer of integrated convolutional neuralnetwork comprises an amplification network and J layers of convolutionalneural networks, J being an integer larger than or equal to two.Hereinafter, for convenience of description, the integratedconvolutional neural network shown in FIG. 4B is referred to as theintegrated convolutional neural network of the k-th layer.

Corresponding to the deep convolutional neural network for facial imagegeneration shown in FIG. 3B, the k-th layer of integrated convolutionalneural network as shown in FIG. 4B receives synthesized images of a(k−1)-th layer and also mapped images of the (k−1)-th layer, k being aninteger larger than or equal to one and smaller than or equal to K.Specifically, the amplification network receives the synthesized imagesof the (k−1)-th layer and the mapped images of the (k−1)-th layer, andamplifies the synthesized images of the (k−1)-th layer and the mappedimages of the (k−1)-th layer to generate amplified images, thereafter afirst layer of convolutional neural network receives the amplifiedimages and generates intermediate images of the first layer; likewise, aj-th layer of convolutional neural network receives intermediate imagesof a (j−1)-th layer from the (j−1)-th layer of convolutional neuralnetwork and generates intermediate images of a j-th layer, a size of theintermediate images of the j-th layer is the same as a size of theintermediate images of the (j−1)-th layer, and a number (channel number)of the intermediate images of the j-th layer may be smaller than, equalto, or larger than a number (channel number) of the intermediate imagesof the (j−1)-th layer, j being an integer larger than or equal to twoand smaller than or equal to J; last, a J-th layer of convolutionalneural network receives intermediate images of an (J−1)-th layer andgenerates intermediate images of a J-th layer, the intermediate imagesof the J-th layer is taken as the synthesized images of the k-th layeroutputted by the k-th layer of integrated convolutional neural network.

FIG. 4C shows another schematic diagram of structure of an integratedconvolutional neural network corresponding to FIG. 3B according to anembodiment of the present disclosure.

Different than inputting the mapped images of the (k−1)-th layer intothe amplification network shown in FIG. 4B, the mapped images of the(k−1)-th layer are inputted into the first layer of convolutional neuralnetwork in FIG. 4C. In this case, a size of the mapped images of the(k−1)-th layer is the same as a size of the amplified images outputtedby the amplification network. The first layer of convolutional neuralnetwork receives the amplified images and the mapped images of the(k−1)-th layer and generates intermediate images of the first layer;likewise a j-th layer of convolutional neural network receivesintermediate images of a (j−1)-th layer and generates intermediateimages of the j-th layer, last, an J-th layer of convolutional neuralnetwork receives intermediate images of a (J−1)-th layer and generatesintermediate images of the J-th layer, the intermediate images of theJ-th layer is taken as synthesized images of the k-th layer outputted bythe k-th layer of integrated convolutional neural network.

Optionally, besides the first layer of convolutional neural network, themapped images of the (k−1)-th layer may be also inputted to any layeramong the J layers of convolutional neural network. It should be notedthat, no matter the mapped images of the (k−1)-th layer are inputted towhich layer of convolutional neural network, a size of the mapped imagesof the (k−1)-th layer inputted to said layer is the same as a size ofthe intermediate images inputted to said layer.

According to an embodiment of the present disclosure, after the deepconvolutional network for facial image generation generates thesynthesized facial image, the generated facial image is furtherevaluated, and, optionally, parameters of the deep convolutional networkfor facial image generation and the deep convolutional network for facedetermination can be updated according to an evaluation result.

FIG. 5A shows a schematic diagram of the principle of facial imagegeneration and determination process according to a first embodiment ofthe present disclosure.

In a first embodiment of the present application, the facial featurevector is extracted from a given facial image by use of a deepconvolutional network for facial feature extraction.

FIG. 6 shows a schematic diagram of structure of a deep convolutionalnetwork for facial feature extraction according to the first embodimentof the present disclosure.

According to the first embodiment of the present disclosure, the deepconvolutional network for facial feature extraction comprises: P layersof convolutional neural network and at least one layer of fullyconnected neural network, P being an integer larger than or equal totwo.

A first layer of convolutional neural network of the P layers ofconvolutional neural network is used to receive the given facial image,the given facial image for example is three (channels) given facialimages with a size of 128×128, for example, given facial images with asize of 128×128 in R channel, G channel, and B channel, a number(channel number) of the intermediate images generated by the first layerof convolutional neural network is bigger than a number (channel number)of the given facial image, and a size of the intermediate imagesgenerated by the first layer of convolutional neural network is smallerthan a size of the given facial image, a P-th layer of convolutionalneural network outputs a plurality of small images, for example 128small images whose size is 4×4 or 8×8. Last, the at least one layer offully connected neural network is used to receive the plurality of smallimages outputted from the P-th layer of convolutional neural network andgenerate the facial feature vector.

According to the first embodiment of the present disclosure, after thedeep convolutional neural network for facial image generation generatesthe synthesized facial image, a demand satisfaction score may begenerated based on the synthesized facial image and the demanded featurevector by use of a deep convolutional network for demand determination,so as to determine whether the synthesized facial image satisfiesrequirements of the demanded feature vector. In addition, further,parameters of the deep convolutional network for facial featureextraction, the deep convolutional network for facial image generation,and the deep convolutional network for demand determination may beupdated based on the demand satisfaction score.

In addition, according to the first embodiment of the presentdisclosure, after the deep convolutional neural network for facial imagegeneration generates the synthesized facial image, a face matching scoremay be generated based on the synthesized facial image and the givenfacial image by use of a deep convolutional network for facedetermination, so as to determine whether the synthesized facial imageand the given facial image belong to the same face. In addition,further, parameters of the deep convolutional network for facial featureextraction, the deep convolutional network for facial image generation,and the deep convolutional network for face determination may be updatedbased on the face matching score.

FIG. 5B shows a schematic diagram of the principle of facial imagegeneration and determination process according to a second embodiment ofthe present disclosure.

In a second embodiment of the present disclosure, a facial featurevector is generated randomly.

According to the second embodiment of the present disclosure, after thedeep convolutional neural network for facial image generation generatesthe synthesized facial image, a face satisfaction score may be generatedbased on the synthesized facial image by use of a deep convolutionalnetwork for face determination. In addition, further, parameters of thedeep convolutional network for facial image generation and the deepconvolutional network for face determination may be updated based on theface satisfaction score.

In addition, according to the second embodiment of the presentdisclosure, after the deep convolutional neural network for facial imagegeneration generates the synthesized facial image, a demand satisfactionscore may be generated based on the synthesized facial image and thedemanded feature vector by use of the deep convolutional network fordemand determination, so as to determine whether the synthesized facialimage satisfies requirements of the demanded feature vector. Inaddition, further, parameters of the deep convolutional network forfacial image generation and the deep convolutional network for demanddetermination may be updated based on the demand satisfaction score.

According to an embodiment of the present disclosure, a gradient descentmethod may be used to update parameters of each network, for example,back propagation algorithm may be used to calculate a gradient of eachparameter.

FIG. 7A shows a schematic block diagram of structure of a first deepconvolutional network for face determination according to the firstembodiment of the present disclosure.

As shown in FIG. 7A, the first deep convolutional network for facedetermination comprises a first to-be-determined feature vectorextraction network, a second to-be-determined feature vector extractionnetwork, and a fully connected neural network.

The first to-be-determined feature vector extraction network is used toextract a first to-be-determined feature vector from the given facialimage, the second to-be-determined feature vector extraction network isused to extract a second to-be-determined feature vector from thesynthesized facial image, and the fully connected neural network is usedto generate the face matching score based on the first to-be-determinedfeature vector and the second to-be-determined feature vector.

Parameters of the first to-be-determined feature vector extractionnetwork are the same as parameters of the second to-be-determinedfeature vector extraction network, a dimension number of the firstto-be-determined feature vector is the same as a dimension number of thesecond to-be-determined feature vector, but larger than a dimensionalnumber of the facial feature vector extracted by the deep convolutionalnetwork for facial feature extraction from the given facial image. Forexample, the dimension number of the first to-be-determined featurevector and the dimension number of the second to-be-determined featurevector both are 1000 dimensions, a value range of the face matchingscore is a real number between 0 and 1.

FIG. 7B shows a schematic block diagram of structure of a second deepconvolutional network for face determination according to the secondembodiment of the present disclosure.

As shown in FIG. 7B, the second deep convolutional network for facedetermination comprises a third to-be-determined feature vectorextraction network and a fully connected neural network.

The third to-be-determined feature vector extraction network is used toextract a third to-be-determined feature vector from the synthesizedfacial image, and the fully connected neural network is used to generatethe face satisfaction score based on the third to-be-determined featurevector, wherein a dimension number of the third to-be-determined featurevector is larger than a dimension number of the facial feature vector.

Parameters of the third to-be-determined feature vector extractionnetwork in FIG. 7B may be the same as parameters of the secondto-be-determined feature vector extraction network in FIG. 7A, or thethird to-be-determined feature vector extraction network in FIG. 7B maybe the same network as the second to-be-determined feature vectorextraction network in FIG. 7A, but parameters of the fully connectedneural network in FIG. 7B are different than parameters of the fullyconnected neural network in FIG. 7A.

FIG. 8 shows a schematic diagram of structure of a to-be-determinedfeature vector extraction network according to an embodiment of thepresent disclosure.

According to an embodiment of the present disclosure, a to-be-determinedfeature vector extraction network comprises at least one layer ofconvolutional neural network, at least one layer of locally connectedconvolutional neural network, and at least one layer of fully connectedneural network.

A first layer of convolutional neural network is used to receive aninput image, the at least one layer of convolutional neural network iscascaded, and a last layer of convolutional neural network is connectedto a first layer of locally connected convolutional neural network, theat least one layer of locally connected convolutional neural network iscascaded, a last layer of locally connected convolutional neural networkis connected to a first layer of fully connected neural network, the atleast one fully connected neural network is cascaded, and a last layerof fully connected neural network outputs a to-be-determined featurevector.

The first to-be-determined feature vector extraction network and thesecond to-be-determined feature vector extraction network in FIG. 7Aaccording to the first embodiment of the present disclosure and thethird to-be-determined feature vector extraction network in FIG. 7Baccording to the second embodiment of the present disclosure may adoptthe structure as shown in FIG. 8.

Specifically, the first layer of convolutional neural network of thefirst to-be-determined feature vector extraction network in FIG. 7A isused to receive the given facial image as its input image, and the lastlayer of fully connected neural network thereof outputs the firstto-be-determined feature vector; the first layer of convolutional neuralnetwork of the second to-be-determined feature vector extraction networkin FIG. 7A is used to receive the synthesized facial image as its inputimage, and the last layer of fully connected neural network thereofoutputs the second to-be-determined feature vector.

Specifically, the first layer of convolutional neural network of thethird to-be-determined feature vector extraction network in FIG. 7B isused to receive the synthesized facial image as its input image, and thelast layer of fully connected neural network thereof outputs the thirdto-be-determined feature vector.

FIG. 9 shows a schematic diagram of structure of a deep convolutionalnetwork for demand determination according to an embodiment of thepresent disclosure.

As shown in FIG. 9, the deep convolutional network for demanddetermination according to an embodiment of the present disclosurecomprises Q layers of convolutional neural network and at least onelayer of fully connected neural network.

A first layer of convolutional neural network in the Q layers ofconvolutional neural network is used to receive the synthesized facialimage and the demanded mapped images, a size of the demanded mappedimages is the same as a size of the synthesized facial image, thesynthesized facial image comprises for example three (channels)synthesized facial images with a size of 128×128, that is, thesynthesized facial image with a size of 128×128 in R channel, thesynthesized facial image with a size of 128×128 in G channel, thesynthesized facial image with a size of 128×128 in B channel, thedemanded mapped images comprise N (channels) of demanded mapped imageswith a resolution of 128×128, each dimension in the N-dimensionaldemanded feature vector corresponds to one of the demanded mappedimages, separately, and a value of each pixel in this demanded mappedimage is the value of this dimension. A Q-th layer of convolutionalneural network of the Q layers of convolutional neural network outputs aplurality (channels) of small images whose size is 4×4 or 8×8.

Thereafter, the at least one layer of fully connected neural network isused to generate a demand satisfaction score based on the plurality ofsmall images whose size is 4×4 or 8×8.

In addition, in one exemplary implementation of the embodiment of thepresent disclosure, in the deep convolutional network for facial featureextraction, the deep convolutional network for facial featuregeneration, the integrated convolutional neural network, the deepconvolutional network for face determination, the to-be-determinedfeature vector extraction network, and the deep convolutional networkfor demand determination described above, a non-linear function layer isnested on the last layer of convolutional neural network in each of saidnetworks, and except the last layer of convolutional neural network ineach of said networks, a normalized non-linear function layer is nestedon each layer of convolutional neural network in each of said networks.Those skilled in the art can implement such non-linear function layerand such normalized non-linear functional layer by using the relevantmethods in the prior art, no details are described here, and the presentdisclosure is not subject to limitations of specific normalizationmethods and non-linear functions. The embodiment using this exemplaryimplementation has better technical effect in comparison to otherembodiments, i.e. the synthesized face satisfies particular requirementsmuch more.

FIG. 10 shows a schematic block diagram of a facial image generatingapparatus according to an embodiment of the present disclosure.

A facial image generating apparatus 1000 according to an embodiment ofthe present disclosure comprises a facial feature generating module1010, a demanded feature obtaining module 1020, a vector synthesizingmodule 1030, and a synthesized facial image generating module 1040.

The facial feature generating module 1010 is configured to generate anM-dimensional facial feature vector, M being an integer larger than one.According to an embodiment of the present disclosure, the facial featurevector may be extracted from a given facial image. For example, the deepconvolutional network for facial feature extraction according to anembodiment of the present disclosure may be used to extract the facialfeature vector from a given facial image. Alternatively, according to anembodiment of the present disclosure, the facial feature vector may begenerated randomly, in this case, the facial feature vector represents arandomly generated face.

The demanded feature obtaining module 1020 is configured to obtain anN-dimensional demanded feature vector, N being an integer larger than orequal to one. For example, a value of each dimension of theN-dimensional demanded feature vector is a real number in a range of(−1,1) or a real number in a range of (0,1), and represents a differentdemand, for example, whether glasses were worn, whether there are bangs,light intensity, face rotation angle and so on.

The vector synthesizing module 1030 is configured to link theM-dimensional facial feature vector with the N-dimensional demandedfeature vector to generate a synthesized feature vector. According to anembodiment of the present disclosure, the M-dimensional facial featurevector is linked with the N-dimensional demanded feature vector togenerate an (M+N)-dimensional synthesized feature vector. For example,the N-dimensional demanded feature vector may be placed after theM-dimensional facial feature vector or before M-dimensional facialfeature vector, or may be inserted into the M-dimensional facial featurevector.

The synthesized facial image generating module 1040 is configured togenerate a synthesized facial image by use of a deep convolutionalnetwork for facial image generation and based on the synthesized featurevector. Structure of the deep convolutional network for facial imagegeneration is as shown in FIGS. 3A and 3B, no more details are repeatedhere.

FIG. 11A shows a schematic block diagram of a facial image generatingand determining apparatus according to the first embodiment of thepresent disclosure.

The facial image generating and determining apparatus according to anembodiment of the present disclosure comprises a facial featuregenerating module 1110, a demanded feature obtaining module 1020, avector synthesizing module 1030, a synthesized facial image generatingmodule 1040, a first face determining module 1150, a second parameteradjusting module 1160, a demand determining module 1170, and a firstparameter adjusting module 1180.

The facial feature generating module 1110 is configured to extract thefacial feature vector from a given facial image and by use of a deepconvolutional network for facial feature extraction. Structure of thedeep convolutional network for facial feature extraction is as shown inFIG. 6, no details are repeated here.

The first face determining module 1150 is configured to generate a facematching score based on the synthesized facial image and the givenfacial image and by use of a first deep convolutional network for facedetermination, so as to determine whether the synthesized facial imageand the given facial image belong to the same face. Structure of thedeep convolutional network for face determination is as shown in FIGS.7A and 8, no details are repeated here.

The demand determining module 1170 is configured to generate a demandsatisfaction score based on the synthesized facial image and thedemanded feature vector and by use of a deep convolutional network fordemand determination, so as to determine whether the synthesized facialimage satisfies requirements of the demanded feature vector. Structureof the deep convolutional network for demand determination is as shownin FIG. 9, no details are repeated here.

The second parameter adjusting module 1160 is configured to updateparameters of the deep convolutional network for facial featureextraction, the deep convolutional network for facial image generation,and the first deep convolutional network for face determination based onthe face matching score. According to an embodiment of the presentdisclosure, the second parameter adjusting module 1160 may use agradient descent method or the like to update parameters of therespective networks.

The first parameter adjusting module 1180 is configured to updateparameters of the deep convolutional network for facial image generationand the deep convolutional network for demand determination based on thedemand satisfaction score. According to an embodiment of the presentdisclosure, the first parameter adjusting module 1180 may use a gradientdescent method or the like to update parameters of the respectivenetworks.

FIG. 11B shows a schematic block diagram of a facial image generatingand determining apparatus according to the second embodiment of thepresent disclosure.

The facial image generating and determining apparatus according to anembodiment of the present disclosure comprises a facial featuregenerating module 1115, a demanded feature obtaining module 1020, avector synthesizing module 1030, a synthesized facial image generatingmodule 1040, a second face determining module 1155, a third parameteradjusting module 1165, a demand determining module 1170, and a firstparameter adjusting module 1180.

The facial feature generating module 1115 is configured to randomlygenerate the facial feature vector.

The second face determining module 1155 is configured to generate a facesatisfaction score based on the synthesized facial image and by use of asecond deep convolutional network for face determination, so as todetermine whether the synthesized facial image is a facial image.Structure of the deep convolutional network for face determination is asshown in FIGS. 7A and 8, no details are repeated here.

The demand determining module 1170 is configured to generate a demandsatisfaction score based on the synthesized facial image and thedemanded mapped images generated from the demanded feature vector and byuse of a deep convolutional network for demand determination, so as todetermine whether the synthesized facial image satisfies requirements ofthe demanded feature vector. Structure of the deep convolutional networkfor demand determination is as shown in FIG. 9, no details are repeatedhere.

The third parameter adjusting module 1165 is configured to updateparameters of the deep convolutional network for facial image generationand the second deep convolutional network for face determination basedon the face satisfaction score. According to an embodiment of thepresent disclosure, the third parameter adjusting module 1165 may use agradient descent method or the like to update parameters of therespective networks.

The first parameter adjusting module 1180 is configured to updateparameters of the deep convolutional network for facial image generationand the deep convolutional network for demand determination based on thedemand satisfaction score. According to an embodiment of the presentdisclosure, the first parameter adjusting module 1180 may use a gradientdescent method or the like to update parameters of the respectivenetworks.

FIG. 12 shows a schematic block diagram of structure of an electronicdevice in which a facial image generating and determining apparatusaccording to an embodiment of the present disclosure is implemented.

The electronic device comprises one or more processors 1210, a memorydevice 1220, an input device 1230, and an output device 1240, thesecomponents are interconnected via a bus system 1280 and/or other formsof connection mechanism (not shown). It should be noted that thecomponents and structure of the electronic device shown in FIG. 12 aremerely exemplary, rather than restrictive, the electronic device mayalso have other components and structures as desired.

The processor 1210 may be a central processing unit (CPU) or other formsof processing unit having data processing capability and/or instructionexecuting capability.

The storage device 1220 may include one or more computer programproducts, the computer program product may include various forms ofcomputer readable storage medium, such as volatile memory and/ornon-volatile memory. The volatile memory may include, for example,random access memory (RAM) and/or cache. The non-volatile memory mayinclude, for example, read only memory (ROM), hard disk, flash memory.One or more computer program instructions may be stored on thecomputer-readable storage medium, and the processor 1210 can run theprogram instructions to implement the functions described above in theembodiments of the present disclosure (implemented by the processor)and/or other intended functions. Various applications and data, such asthe given face image, the synthesized facial image, the demanded featurevector etc., as well as various data used and/or generated by theapplications, may also be stored in the computer-readable storagemedium.

The input device 1230 may include a device for inputting the givenfacial image or the demanded feature vector, such as a keyboard.

The output device 1240 may include a display to output the synthesizedfacial image and/or various score results, and may also include aspeaker or the like to output various score results.

The computer program instructions stored in the storage device 1220 canbe executed by the processor 1210 to implement the facial imagegenerating method and apparatus as described above, and the face imagegenerating and determining method and apparatus as described above, andthe deep convolutional network for facial feature extraction, the deepconvolutional network for facial image generation, the deepconvolutional network for face determination, and the deep convolutionalnetwork for demand determination in particular as described above.

As will be appreciated, according to an embodiment of the presentdisclosure, a facial image is generated by use of a deep convolutionalnetwork for facial image generation and based on a facial feature vectorand a demanded feature vector, so a facial image with a specific featureprescribed by the demanded feature vector can be generated fast withoutusing the three-dimensional model.

In addition, according to an embodiment of the present disclosure, aftera synthesized facial image is generated, whether the generatedsynthesized facial image satisfies requirements of the demanded featurevector is determined and the corresponding demand satisfaction score isgenerated by use of the deep convolutional network for demanddetermination, and further, parameters of the deep convolutional networkfor facial image generation and the deep convolutional network fordemand determination are updated based on the demand satisfaction score,thereby the deep convolutional network for facial image generation andthe deep convolutional network for demand determination can be trainedin synchronization.

In addition, according to an embodiment of the present disclosure, aftera synthesized facial image is generated, whether the generatedsynthesized facial image is a facial image or whether the generatedsynthesized facial image and the given facial image belong to the sameface is determined and the corresponding face satisfaction score or facematching score is generated by use of the deep convolutional network forface determination, and further, parameters of the deep convolutionalnetwork for facial image generation and the deep convolutional networkfor face determination are updated based on the generated facesatisfaction score or the generated face matching score, thereby thedeep convolutional network for facial image generation and the deepconvolutional network for face determination can be trained insynchronization.

Although the exemplary embodiments of the present disclosure have beendescribed with reference to the drawings, as will be appreciated, theabove exemplary embodiments are only illustrative, not intended to limitthe protection scope of the present disclosure. Those of ordinary skillin the art may make many changes, modifications, thereto withoutdeparting from the principle and spirit of the present disclosure, andall of these changes, modifications should fall into the protectionscope of the present disclosure.

1. A facial image generating method, comprising: generating anM-dimensional facial feature vector, M being an integer larger than one;linking the M-dimensional facial feature vector with an N-dimensionaldemanded feature vector to generate a synthesized feature vector, Nbeing an integer larger than or equal to one; and generating asynthesized facial image by use of a deep convolutional network forfacial image generation and based on the synthesized feature vector. 2.The facial image generating method according to claim 1, wherein thedeep convolutional network for facial image generation comprises atleast one layer of fully connected neural network and K layers ofintegrated convolutional neural network, each layer of integratedconvolutional neural network comprising an amplification network and Jlayers of convolutional neural network, K being an integer larger thanor equal to two, and J being an integer larger than or equal to two. 3.The facial image generating method according to claim 2, whereingenerating the synthesized facial image by use of the deep convolutionalnetwork for facial image generation and based on the synthesized featurevector comprises: generating initial synthesized images by use of the atleast one layer of fully connected neural network and based on thesynthesized feature vector; receiving the initial synthesized images andgenerating synthesized images of a first layer by use of the first layerof integrated convolutional neural network, a number of the synthesizedimages of the first layer being smaller than a number of the initialsynthesized images; and receiving synthesized images outputted from a(k−1)-th layer of integrated convolutional neural network and generatingsynthesized images of a k-th layer by use of the k-th layer ofintegrated convolutional neural network, k being an integer larger thanor equal to two and smaller than or equal to K, a size of thesynthesized images of the k-th layer being larger than a size of thesynthesized images of the (k−1)-th layer, and a number of thesynthesized images of the k-th layer being smaller than a number of thesynthesized images of the (k−1)-th layer, wherein synthesized images ofa K-th layer as outputted from the K-th layer of integratedconvolutional neural network is taken as the synthesized facial image.4. The facial image generating method according to claim 2, whereingenerating the synthesized facial image by use of the deep convolutionalnetwork for facial image generation and based on the synthesized featurevector comprises: generating initial synthesized images by use of the atleast one layer of fully connected neural network and based on thesynthesized feature vector; receiving the initial synthesized images andN initial mapped images generated as mapping from the N-dimensionaldemanded feature vector and generating synthesized images of a firstlayer by use of the first layer of integrated convolutional neuralnetwork, a size of the initial synthesized images being the same as asize of the initial mapped images, a number of the synthesized images ofthe first layer being smaller than a number of the initial synthesizedimages; and receiving synthesized images outputted from a (k−1)-th layerof integrated convolutional neural network and N mapped images of the(k−1)-th layer generated as mapping from the N-dimensional demandedfeature vector and generating synthesized images of a k-th layer by useof the k-th layer of integrated convolutional neural network, a size ofthe synthesized images of the (k−1)-th layer being the same as a size ofthe mapped images of the (k−1)-th layer, a size of the synthesizedimages of the k-th layer being larger than a size of the synthesizedimages of the (k−1)-th layer, and a number of the synthesized images ofthe k-th layer being smaller than a number of the synthesized images ofthe (k−1)-th layer, wherein each dimension of the N-dimensional demandedfeature vector is mapped as one of the N initial mapped images and alsomapped as one of N mapped images of the (k−1)-th layer, k being aninteger larger than or equal to two and smaller than or equal to K,wherein synthesized images of a K-th layer as outputted from the K-thlayer of integrated convolutional neural network is taken as thesynthesized facial image.
 5. The facial image generating methodaccording to claim 1, wherein generating the facial feature vectorcomprises: extracting the facial feature vector from a given facialimage by use of a deep convolutional network for facial featureextraction; or randomly generating the facial feature vector.
 6. Thefacial image generating method according to claim 5, wherein the deepconvolutional network for facial feature extraction comprises: P layersof convolutional neural network and at least one layer of fullyconnected neural network, P being an integer larger than or equal totwo, wherein a first layer of convolutional neural network is used toreceive the given facial image, the at least one layer of fullyconnected neural network is used to receive images outputted from a P-thlayer of convolutional neural network and generate the facial featurevector.
 7. The facial image generating method according to claim 5,further comprising: generating a demand satisfaction score based on thesynthesized facial image and the demanded feature vector and by use of adeep convolutional network for demand determination; and updatingparameters of the deep convolutional network for facial image generationand the deep convolutional network for demand determination based on thedemand satisfaction score.
 8. The facial image generating methodaccording to claim 7, wherein in the case of extracting the facialfeature vector from a given facial image, the facial image generatingmethod further comprises: generating a face matching score based on thesynthesized facial image and the given facial image and by use of afirst deep convolutional network for face determination; and updatingparameters of the deep convolutional network for facial featureextraction, the deep convolutional network for facial image generation,and the first deep convolutional network for face determination based onthe face matching score.
 9. The facial image generating method accordingto claim 7, wherein in the case of randomly generating the facialfeature vector, the facial image generating method further comprises:generating a face satisfaction score based on the synthesized facialimage and by use of a second deep convolutional network for facedetermination; and updating parameters of the deep convolutional networkfor facial image generation and the second deep convolutional networkfor face determination based on the face satisfaction score.
 10. Thefacial image generating method according to claim 8, wherein the firstdeep convolutional network for face determination comprises a firstto-be-determined feature vector extraction network, a secondto-be-determined feature vector extraction network, and a fullyconnected neural network, wherein the first to-be-determined featurevector extraction network is used to extract a first to-be-determinedfeature vector from the given facial image; the second to-be-determinedfeature vector extraction network is used to extract a secondto-be-determined feature vector from the synthesized facial image; thefully connected neural network is used to generate the face matchingscore based on the first to-be-determined feature vector and the secondto-be-determined feature vector, wherein parameters of the firstto-be-determined feature vector extraction network are the same asparameters of the second to-be-determined feature vector extractionnetwork, a dimension number of the first to-be-determined feature vectoris the same as a dimension number of the second to-be-determined featurevector, but larger than a dimensional number of the facial featurevector.
 11. The facial image generating method according to claim 9,wherein the second deep convolutional network for face determinationcomprises a third to-be-determined feature vector extraction network anda fully connected neural network, the third to-be-determined featurevector extraction network is used to extract a third to-be-determinedfeature vector from the synthesized facial image, the fully connectedneural network is used to generate the face satisfaction score based onthe third to-be-determined feature vector, wherein a dimension number ofthe third to-be-determined feature vector is larger than a dimensionnumber of the facial feature vector.
 12. The facial image generatingmethod according to claim 10, wherein each of the to-be-determinedfeature vector extraction networks comprises at least one layer ofconvolutional neural network, at least one layer of locally connectedconvolutional neural network, and at least one layer of fully connectedneural network, wherein a first layer of convolutional neural network isused to receive an input image of the to-be-determined feature vectorextraction network, the at least one layer of convolutional neuralnetwork is cascaded, and a last layer of convolutional neural network isconnected to a first layer of locally connected convolutional neuralnetwork, the at least one layer of locally connected convolutionalneural network is cascaded, a last layer of locally connectedconvolutional neural network is connected to a first layer of fullyconnected neural network, the at least one fully connected neuralnetwork is cascaded, and a last layer of fully connected neural networkoutputs a to-be-determined feature vector of the to-be-determinedfeature vector extraction network.
 13. A facial image generatingapparatus, comprising: a facial feature generating module configured togenerate an M-dimensional facial feature vector, M being an integerlarger than one; a vector synthesizing module configured to link theM-dimensional facial feature vector with an N-dimensional demandedfeature vector to generate a synthesized feature vector, N being aninteger larger than or equal to one; and a synthesized facial imagegenerating module configured to generate a synthesized facial image byuse of a deep convolutional network for facial image generation andbased on the synthesized feature vector.
 14. A facial image generatingdevice, comprising: one or more processors; one or more memories forstoring program instructions, the program instructions being executed bythe processors to perform the following steps: generating anM-dimensional facial feature vector, M being an integer larger than one;linking the M-dimensional facial feature vector with an N-dimensionaldemanded feature vector to generate a synthesized feature vector, Nbeing an integer larger than or equal to one; and generating asynthesized facial image by use of a deep convolutional network forfacial image generation and based on the synthesized feature vector. 15.The facial image generating device according to claim 14, wherein thedeep convolutional network for facial image generation comprises atleast one layer of fully connected neural network and K layers ofintegrated convolutional neural network, each layer of integratedconvolutional neural network comprising an amplification network and Jlayers of convolutional neural network, K being an integer larger thanor equal to two, and J being an integer larger than or equal to two. 16.The facial image generating device according to claim 15, whereingenerating the synthesized facial image by use of the deep convolutionalnetwork for facial image generation and based on the synthesized featurevector comprises: generating initial synthesized images by use of the atleast one layer of fully connected neural network and based on thesynthesized feature vector; receiving the initial synthesized images andgenerating synthesized images of a first layer by use of the first layerof integrated convolutional neural network, a number of the synthesizedimages of the first layer being smaller than a number of the initialsynthesized images; and receiving synthesized images outputted from a(k−1)-th layer of integrated convolutional neural network and generatingsynthesized images of a k-th layer by use of the k-th layer ofintegrated convolutional neural network, k being an integer larger thanor equal to two and smaller than or equal to K, a size of thesynthesized images of the k-th layer being larger than a size of thesynthesized images of the (k−1)-th layer, and a number of thesynthesized images of the k-th layer being smaller than a number of thesynthesized images of the (k−1)-th layer, wherein synthesized images ofa K-th layer as outputted from the K-th layer of integratedconvolutional neural network is taken as the synthesized facial image.17. The facial image generating device according to claim 15, whereingenerating a synthesized facial image by use of a deep convolutionalnetwork for facial image generation and based on the synthesized featurevector comprises: generating an initial synthesized images by use of theat least one layer of fully connected neural network and based on thesynthesized feature vector; receiving the initial synthesized images andN initial mapped images generated as mapping from the N-dimensionaldemanded feature vector and generating synthesized images of a firstlayer by use of the first layer of integrated convolutional neuralnetwork, a size of the initial synthesized images being the same as asize of the initial mapped images, a number of the synthesized images ofthe first layer being smaller than a number of the initial synthesizedimages; and receiving synthesized images outputted from a (k−1)-th layerof integrated convolutional neural network and N mapped images of the(k−1)-th layer generated as mapping from the N-dimensional demandedfeature vector and generating synthesized images of a k-th layer by useof the k-th layer of integrated convolutional neural network, a size ofthe synthesized images of the (k−1)-th layer being the same as a size ofthe mapped images of the (k−1)-th layer, a size of the synthesizedimages of the k-th layer being larger than a size of the synthesizedimages of the (k−1)-th layer, and a number of the synthesized images ofthe k-th layer being smaller than a number of the synthesized images ofthe (k−1)-th layer, wherein each dimension of the N-dimensional demandedfeature vector is mapped as one of the N initial mapped images and alsomapped as one of N mapped images of the (k−1)-th layer, k being aninteger larger than or equal to 2 and smaller than or equal to K,wherein synthesized images of a K-th layer as outputted from the K-thlayer of integrated convolutional neural network is taken as thesynthesized facial image.
 18. The facial image generating deviceaccording to claim 14, wherein generating the facial feature vectorcomprises: extracting the facial feature vector from a given facialimage by use of a deep convolutional network for facial featureextraction; or randomly generating the facial feature vector.
 19. Thefacial image generating device according to claim 14, wherein theprocessors execute the program instructions further for: generating ademand satisfaction score based on the synthesized facial image and thedemanded feature vector and by use of a deep convolutional network fordemand determination; and updating parameters of the deep convolutionalnetwork for facial image generation and the deep convolutional networkfor demand determination based on the demand satisfaction score.